Scientists waging war against breast cancer have a new reason to smile.

Researchers at Lawrence Berkeley National Laboratory and Carnegie Mellon University have developed a sophisticated computational method to determine how gene networks are “rewired” at the moment healthy breast cells turn malignant. It also can determine how the cells respond to potential cancer therapy treatments.

The computational model showed researchers how breast cancer cells develop resistance to certain treatments of drugs being evaluated by scientists for the possible treatment of the disease.

Over 230,000 women were diagnosed with breast cancer in the U.S. alone last year, and one in eight American women will contract the disease, according to BreastCancer.org.

“With our system, pharmaceutical developers wouldn’t need to go to expensive clinical trials to discover that a drug isn’t going to work,” said Wei Wu, associate research professor in CMU’s Lane Center for Computational Biology, which participated in the research.

“It could save them a tremendous amount of money and a tremendous amount of time,” Wu said.

The Lane Center is, you guessed it, named after former Oracle senior executive Ray Lane. Lane is a partner “emeritus” with Valley VC heavyweight Kleiner Perkins Caufield & Byers and sits on the board of trustees at CMU.

The upside, ultimately, means new potential avenues of treatment and “promising molecular targets for drug therapy,” according to researchers.

Wu worked on the project with colleague Eric Xing of CMU’s Machine Learning Department, and Mina Bissell, a noted breast cancer researcher at the Berkeley Lab. Funding for the complex study was provided by the National Institute of General Medical Sciences, the National Cancer Institute, and the U.S. Department of Defense Breast Cancer Research Program.

Breast cancer cells were studied with a 3D “cell culturing technique” created by the Berkeley Lab. According to researchers:

“These (gene regulatory networks) can be inferred based on microarrays, which measure the expression levels of tens of thousands of genes in a cell. But the number of microarrays that investigators can afford to run for each cell state — normal cells, malignant cells and malignant cells that have reverted to normal-looking cells that also are organized normally — is limited. So researchers often pool microarray data from several cell states in hopes of gaining enough samples to draw solid conclusions about networks.

That approach wouldn’t work in a study that sought to differentiate between the various cell states. But Xing’s research group had developed a computational method called Treegl that can detect multiple networks by examining the relationships between the cell types. The method pools microarray data to build statistical power in similar samples in which the gene regulatory networks appear similar while also taking the differences into account.”

One of the most promising facets, so far, of the computational-based research is the potential for eliminating drugs that, while showing initial promise, tend to be flawed in the long term. Another benefit, according to Wu, is the ability to use fewer people in clinical trials, thus saving time and money.